Overview

Dataset statistics

Number of variables16
Number of observations2297
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory287.2 KiB
Average record size in memory128.1 B

Variable types

Numeric16

Alerts

df_index is highly correlated with Run and 8 other fieldsHigh correlation
Run is highly correlated with df_index and 8 other fieldsHigh correlation
Reboiler duty (W) is highly correlated with CO2 emitted while capturing (kmol/hr) and 1 other fieldsHigh correlation
Solvent circulation (kmol/hr) is highly correlated with CO2 captured (kmol/hr) and 3 other fieldsHigh correlation
CO2 in feed (kmol/hr) is highly correlated with df_index and 9 other fieldsHigh correlation
Reboiler emission/capture emissions is highly correlated with df_index and 7 other fieldsHigh correlation
CO2 captured (kmol/hr) is highly correlated with Solvent circulation (kmol/hr) and 9 other fieldsHigh correlation
CO2 emitted while capturing (kmol/hr) is highly correlated with Reboiler duty (W) and 4 other fieldsHigh correlation
CO2 net captured (kmol/hr) is highly correlated with Solvent circulation (kmol/hr) and 9 other fieldsHigh correlation
CO2 net captured (%) is highly correlated with df_index and 2 other fieldsHigh correlation
MWh/ton CO2 net captured is highly correlated with df_index and 9 other fieldsHigh correlation
CO2 emitted/captured (%) is highly correlated with df_index and 9 other fieldsHigh correlation
CO2 conc. To storage (mol%) is highly correlated with df_index and 8 other fieldsHigh correlation
CO2 in feed (mol%) is highly correlated with df_index and 9 other fieldsHigh correlation
CO2 in acid gas (kmol/hr) is highly correlated with df_index and 8 other fieldsHigh correlation
CO2 from reboiler (kmol/hr) is highly correlated with Reboiler duty (W) and 1 other fieldsHigh correlation
df_index is highly correlated with Run and 8 other fieldsHigh correlation
Run is highly correlated with df_index and 8 other fieldsHigh correlation
Reboiler duty (W) is highly correlated with df_index and 4 other fieldsHigh correlation
Solvent circulation (kmol/hr) is highly correlated with Reboiler duty (W) and 5 other fieldsHigh correlation
CO2 in feed (kmol/hr) is highly correlated with df_index and 8 other fieldsHigh correlation
Reboiler emission/capture emissions is highly correlated with df_index and 5 other fieldsHigh correlation
CO2 captured (kmol/hr) is highly correlated with Solvent circulation (kmol/hr) and 5 other fieldsHigh correlation
CO2 emitted while capturing (kmol/hr) is highly correlated with Reboiler duty (W) and 4 other fieldsHigh correlation
CO2 net captured (kmol/hr) is highly correlated with Solvent circulation (kmol/hr) and 7 other fieldsHigh correlation
CO2 net captured (%) is highly correlated with df_index and 2 other fieldsHigh correlation
MWh/ton CO2 net captured is highly correlated with CO2 emitted/captured (%)High correlation
CO2 emitted/captured (%) is highly correlated with df_index and 7 other fieldsHigh correlation
CO2 conc. To storage (mol%) is highly correlated with CO2 in feed (kmol/hr) and 4 other fieldsHigh correlation
CO2 in feed (mol%) is highly correlated with df_index and 8 other fieldsHigh correlation
CO2 in acid gas (kmol/hr) is highly correlated with df_index and 7 other fieldsHigh correlation
CO2 from reboiler (kmol/hr) is highly correlated with df_index and 4 other fieldsHigh correlation
df_index is highly correlated with Run and 6 other fieldsHigh correlation
Run is highly correlated with df_index and 6 other fieldsHigh correlation
Reboiler duty (W) is highly correlated with CO2 emitted while capturing (kmol/hr) and 1 other fieldsHigh correlation
Solvent circulation (kmol/hr) is highly correlated with CO2 emitted while capturing (kmol/hr) and 1 other fieldsHigh correlation
CO2 in feed (kmol/hr) is highly correlated with df_index and 8 other fieldsHigh correlation
Reboiler emission/capture emissions is highly correlated with MWh/ton CO2 net captured and 1 other fieldsHigh correlation
CO2 captured (kmol/hr) is highly correlated with CO2 in feed (kmol/hr) and 3 other fieldsHigh correlation
CO2 emitted while capturing (kmol/hr) is highly correlated with Reboiler duty (W) and 4 other fieldsHigh correlation
CO2 net captured (kmol/hr) is highly correlated with CO2 in feed (kmol/hr) and 3 other fieldsHigh correlation
CO2 net captured (%) is highly correlated with Solvent circulation (kmol/hr)High correlation
MWh/ton CO2 net captured is highly correlated with df_index and 7 other fieldsHigh correlation
CO2 emitted/captured (%) is highly correlated with df_index and 7 other fieldsHigh correlation
CO2 conc. To storage (mol%) is highly correlated with df_index and 6 other fieldsHigh correlation
CO2 in feed (mol%) is highly correlated with df_index and 8 other fieldsHigh correlation
CO2 in acid gas (kmol/hr) is highly correlated with df_index and 6 other fieldsHigh correlation
CO2 from reboiler (kmol/hr) is highly correlated with Reboiler duty (W) and 1 other fieldsHigh correlation
df_index is highly correlated with Run and 13 other fieldsHigh correlation
Run is highly correlated with df_index and 13 other fieldsHigh correlation
Reboiler duty (W) is highly correlated with df_index and 8 other fieldsHigh correlation
Solvent circulation (kmol/hr) is highly correlated with df_index and 8 other fieldsHigh correlation
CO2 in feed (kmol/hr) is highly correlated with df_index and 7 other fieldsHigh correlation
Reboiler emission/capture emissions is highly correlated with df_index and 11 other fieldsHigh correlation
CO2 captured (kmol/hr) is highly correlated with df_index and 13 other fieldsHigh correlation
CO2 emitted while capturing (kmol/hr) is highly correlated with df_index and 10 other fieldsHigh correlation
CO2 net captured (kmol/hr) is highly correlated with df_index and 11 other fieldsHigh correlation
CO2 net captured (%) is highly correlated with df_index and 12 other fieldsHigh correlation
MWh/ton CO2 net captured is highly correlated with CO2 net captured (%) and 2 other fieldsHigh correlation
CO2 emitted/captured (%) is highly correlated with df_index and 13 other fieldsHigh correlation
CO2 conc. To storage (mol%) is highly correlated with df_index and 10 other fieldsHigh correlation
CO2 in feed (mol%) is highly correlated with df_index and 7 other fieldsHigh correlation
CO2 in acid gas (kmol/hr) is highly correlated with df_index and 10 other fieldsHigh correlation
CO2 from reboiler (kmol/hr) is highly correlated with df_index and 8 other fieldsHigh correlation
df_index has unique values Unique
Run has unique values Unique
Reboiler emission/capture emissions has unique values Unique
CO2 captured (kmol/hr) has unique values Unique
CO2 emitted while capturing (kmol/hr) has unique values Unique
CO2 net captured (kmol/hr) has unique values Unique
CO2 net captured (%) has unique values Unique
MWh/ton CO2 net captured has unique values Unique
CO2 emitted/captured (%) has unique values Unique
CO2 conc. To storage (mol%) has unique values Unique

Reproduction

Analysis started2023-08-27 07:37:16.220999
Analysis finished2023-08-27 07:38:13.701167
Duration57.48 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1329.422725
Minimum0
Maximum2482
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:13.864861image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile291.8
Q1760
median1334
Q31908
95-th percentile2367.2
Maximum2482
Range2482
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation671.1404014
Coefficient of variation (CV)0.5048359627
Kurtosis-1.143506371
Mean1329.422725
Median Absolute Deviation (MAD)574
Skewness-0.04046453472
Sum3053684
Variance450429.4384
MonotonicityStrictly increasing
2023-08-27T15:38:14.133106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
17201
 
< 0.1%
17141
 
< 0.1%
17151
 
< 0.1%
17161
 
< 0.1%
17171
 
< 0.1%
17181
 
< 0.1%
17191
 
< 0.1%
17211
 
< 0.1%
17121
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
24821
< 0.1%
24811
< 0.1%
24801
< 0.1%
24791
< 0.1%
24781
< 0.1%
24771
< 0.1%
24761
< 0.1%
24751
< 0.1%
24741
< 0.1%
24731
< 0.1%

Run
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1330.422725
Minimum1
Maximum2483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:14.362710image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile292.8
Q1761
median1335
Q31909
95-th percentile2368.2
Maximum2483
Range2482
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation671.1404014
Coefficient of variation (CV)0.5044565074
Kurtosis-1.143506371
Mean1330.422725
Median Absolute Deviation (MAD)574
Skewness-0.04046453472
Sum3055981
Variance450429.4384
MonotonicityStrictly increasing
2023-08-27T15:38:14.558977image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
17211
 
< 0.1%
17151
 
< 0.1%
17161
 
< 0.1%
17171
 
< 0.1%
17181
 
< 0.1%
17191
 
< 0.1%
17201
 
< 0.1%
17221
 
< 0.1%
17131
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
121
< 0.1%
131
< 0.1%
141
< 0.1%
ValueCountFrequency (%)
24831
< 0.1%
24821
< 0.1%
24811
< 0.1%
24801
< 0.1%
24791
< 0.1%
24781
< 0.1%
24771
< 0.1%
24761
< 0.1%
24751
< 0.1%
24741
< 0.1%

Reboiler duty (W)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1428167.175
Minimum600000
Maximum5000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:14.733546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum600000
5-th percentile600000
Q1800000
median1050000
Q31300000
95-th percentile4500000
Maximum5000000
Range4400000
Interquartile range (IQR)500000

Descriptive statistics

Standard deviation1079509.54
Coefficient of variation (CV)0.7558705725
Kurtosis3.106547598
Mean1428167.175
Median Absolute Deviation (MAD)250000
Skewness2.039477973
Sum3280500000
Variance1.165340847 × 1012
MonotonicityNot monotonic
2023-08-27T15:38:14.902233image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1000000178
 
7.7%
800000117
 
5.1%
1250000117
 
5.1%
1200000117
 
5.1%
1150000117
 
5.1%
1100000117
 
5.1%
1050000117
 
5.1%
950000117
 
5.1%
900000117
 
5.1%
850000117
 
5.1%
Other values (13)1066
46.4%
ValueCountFrequency (%)
600000117
5.1%
650000117
5.1%
700000117
5.1%
750000116
5.1%
800000117
5.1%
850000117
5.1%
900000117
5.1%
950000117
5.1%
1000000178
7.7%
1050000117
5.1%
ValueCountFrequency (%)
500000058
2.5%
450000058
2.5%
400000060
2.6%
350000060
2.6%
300000060
2.6%
250000062
2.7%
200000062
2.7%
150000062
2.7%
1300000117
5.1%
1250000117
5.1%

Solvent circulation (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.26033957
Minimum10
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:15.086854image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q130
median45
Q365
95-th percentile160
Maximum200
Range190
Interquartile range (IQR)35

Descriptive statistics

Standard deviation41.47764141
Coefficient of variation (CV)0.7372447754
Kurtosis2.677067801
Mean56.26033957
Median Absolute Deviation (MAD)20
Skewness1.684163832
Sum129230
Variance1720.394737
MonotonicityNot monotonic
2023-08-27T15:38:15.230655image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
40214
 
9.3%
60214
 
9.3%
65135
 
5.9%
55135
 
5.9%
50135
 
5.9%
45135
 
5.9%
35135
 
5.9%
30135
 
5.9%
25135
 
5.9%
20135
 
5.9%
Other values (10)789
34.3%
ValueCountFrequency (%)
10134
5.8%
15135
5.9%
20135
5.9%
25135
5.9%
30135
5.9%
35135
5.9%
40214
9.3%
45135
5.9%
50135
5.9%
55135
5.9%
ValueCountFrequency (%)
20036
 
1.6%
18048
 
2.1%
16057
 
2.5%
14054
 
2.4%
12054
 
2.4%
10063
 
2.7%
8073
 
3.2%
70135
5.9%
65135
5.9%
60214
9.3%

CO2 in feed (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.8402264
Minimum50
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:15.401280image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q1150
median250
Q3350
95-th percentile450
Maximum450
Range400
Interquartile range (IQR)200

Descriptive statistics

Standard deviation127.0820991
Coefficient of variation (CV)0.4872028401
Kurtosis-1.178341957
Mean260.8402264
Median Absolute Deviation (MAD)100
Skewness-0.1092360214
Sum599150
Variance16149.85991
MonotonicityNot monotonic
2023-08-27T15:38:15.552613image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
300276
12.0%
350276
12.0%
450276
12.0%
400275
12.0%
250267
11.6%
200253
11.0%
150231
10.1%
100228
9.9%
50215
9.4%
ValueCountFrequency (%)
50215
9.4%
100228
9.9%
150231
10.1%
200253
11.0%
250267
11.6%
300276
12.0%
350276
12.0%
400275
12.0%
450276
12.0%
ValueCountFrequency (%)
450276
12.0%
400275
12.0%
350276
12.0%
300276
12.0%
250267
11.6%
200253
11.0%
150231
10.1%
100228
9.9%
50215
9.4%

Reboiler emission/capture emissions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4389038567
Minimum0.1379824374
Maximum0.7853766594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:15.760551image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1379824374
5-th percentile0.2565271758
Q10.3480690892
median0.433338751
Q30.5252151122
95-th percentile0.6394545214
Maximum0.7853766594
Range0.647394222
Interquartile range (IQR)0.177146023

Descriptive statistics

Standard deviation0.1190165412
Coefficient of variation (CV)0.2711676815
Kurtosis-0.5283401566
Mean0.4389038567
Median Absolute Deviation (MAD)0.08846126507
Skewness0.1687366535
Sum1008.162159
Variance0.01416493708
MonotonicityNot monotonic
2023-08-27T15:38:15.958298image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57530294071
 
< 0.1%
0.25052294361
 
< 0.1%
0.27726296611
 
< 0.1%
0.26677210491
 
< 0.1%
0.25573365981
 
< 0.1%
0.24047748781
 
< 0.1%
0.21671894151
 
< 0.1%
0.23706845171
 
< 0.1%
0.26220080491
 
< 0.1%
0.29669769961
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
0.13798243741
< 0.1%
0.14370380321
< 0.1%
0.14567120271
< 0.1%
0.14994941371
< 0.1%
0.15190500681
< 0.1%
0.15360295381
< 0.1%
0.156835351
< 0.1%
0.15876143151
< 0.1%
0.16034385361
< 0.1%
0.16568203151
< 0.1%
ValueCountFrequency (%)
0.78537665941
< 0.1%
0.7713465521
< 0.1%
0.76510115151
< 0.1%
0.76125952141
< 0.1%
0.75340461651
< 0.1%
0.75257075011
< 0.1%
0.74636540311
< 0.1%
0.7448677721
< 0.1%
0.74370147551
< 0.1%
0.73857896651
< 0.1%

CO2 captured (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.43891874
Minimum16.25319284
Maximum206.7390375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:16.191879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum16.25319284
5-th percentile22.41623427
Q146.08059727
median78.399494
Q3114.5290926
95-th percentile172.5643738
Maximum206.7390375
Range190.4858447
Interquartile range (IQR)68.4484953

Descriptive statistics

Standard deviation45.17570676
Coefficient of variation (CV)0.5350104837
Kurtosis-0.3198173543
Mean84.43891874
Median Absolute Deviation (MAD)33.84204796
Skewness0.5937698854
Sum193956.1963
Variance2040.844481
MonotonicityNot monotonic
2023-08-27T15:38:16.436743image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.052153991
 
< 0.1%
102.25920011
 
< 0.1%
108.41108281
 
< 0.1%
107.28106771
 
< 0.1%
106.13585291
 
< 0.1%
104.9023641
 
< 0.1%
103.55178311
 
< 0.1%
100.9661431
 
< 0.1%
103.47008171
 
< 0.1%
110.67703581
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
16.253192841
< 0.1%
16.256991191
< 0.1%
16.260112151
< 0.1%
16.262638581
< 0.1%
16.264742081
< 0.1%
16.266529041
< 0.1%
16.26806981
< 0.1%
16.269414611
< 0.1%
16.270599181
< 0.1%
16.271651731
< 0.1%
ValueCountFrequency (%)
206.73903751
< 0.1%
206.72288641
< 0.1%
206.65520141
< 0.1%
206.63371211
< 0.1%
206.24866521
< 0.1%
205.89610571
< 0.1%
205.87716981
< 0.1%
205.85974761
< 0.1%
205.69662461
< 0.1%
205.23800191
< 0.1%

CO2 emitted while capturing (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.13914451
Minimum3.967575333
Maximum40.28987713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:16.687717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum3.967575333
5-th percentile5.919912662
Q18.081988405
median10.49368878
Q314.33998992
95-th percentile30.75854103
Maximum40.28987713
Range36.3223018
Interquartile range (IQR)6.258001513

Descriptive statistics

Standard deviation7.625618832
Coefficient of variation (CV)0.5803740743
Kurtosis1.421902686
Mean13.13914451
Median Absolute Deviation (MAD)2.815045785
Skewness1.519265799
Sum30180.61494
Variance58.15006257
MonotonicityNot monotonic
2023-08-27T15:38:16.936692image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1108781541
 
< 0.1%
10.614161181
 
< 0.1%
11.803693951
 
< 0.1%
11.501134361
 
< 0.1%
11.197729581
 
< 0.1%
11.057546031
 
< 0.1%
11.325938711
 
< 0.1%
10.353741461
 
< 0.1%
10.92153921
 
< 0.1%
12.4093251
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
3.9675753331
< 0.1%
4.1951136471
< 0.1%
4.3044627011
< 0.1%
4.3996263341
< 0.1%
4.4226013131
< 0.1%
4.5202518711
< 0.1%
4.5331169311
< 0.1%
4.6026034911
< 0.1%
4.648911791
< 0.1%
4.6500388931
< 0.1%
ValueCountFrequency (%)
40.289877131
< 0.1%
39.841945081
< 0.1%
39.388364741
< 0.1%
38.976092221
< 0.1%
38.922093541
< 0.1%
38.530089021
< 0.1%
38.428676671
< 0.1%
38.080301551
< 0.1%
37.89857541
< 0.1%
37.874420721
< 0.1%

CO2 net captured (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.29977423
Minimum1.841799633
Maximum171.9075639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:17.181305image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.841799633
5-th percentile15.15267244
Q137.33256114
median67.16710679
Q399.33153078
95-th percentile146.4472437
Maximum171.9075639
Range170.0657642
Interquartile range (IQR)61.99896963

Descriptive statistics

Standard deviation39.54133227
Coefficient of variation (CV)0.5545786462
Kurtosis-0.5162593889
Mean71.29977423
Median Absolute Deviation (MAD)31.09342931
Skewness0.4731805555
Sum163775.5814
Variance1563.516957
MonotonicityNot monotonic
2023-08-27T15:38:17.381058image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.941275841
 
< 0.1%
91.645038951
 
< 0.1%
96.607388881
 
< 0.1%
95.779933331
 
< 0.1%
94.938123321
 
< 0.1%
93.844817921
 
< 0.1%
92.225844341
 
< 0.1%
90.612401571
 
< 0.1%
92.548542461
 
< 0.1%
98.26771081
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
1.8417996331
< 0.1%
2.4576222531
< 0.1%
3.4665988081
< 0.1%
4.1145291291
< 0.1%
4.7301855871
< 0.1%
5.7531422781
< 0.1%
6.387153161
< 0.1%
7.0027203931
< 0.1%
8.025694281
< 0.1%
8.6592501551
< 0.1%
ValueCountFrequency (%)
171.90756391
< 0.1%
171.78705641
< 0.1%
171.74881221
< 0.1%
171.47689471
< 0.1%
171.2063521
< 0.1%
171.14856091
< 0.1%
171.13462981
< 0.1%
171.11663421
< 0.1%
170.95084971
< 0.1%
170.81469661
< 0.1%

CO2 net captured (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.1620131
Minimum3.683599266
Maximum38.20168086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:17.626814image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum3.683599266
5-th percentile11.73716303
Q123.75646162
median30.49653082
Q334.30739323
95-th percentile36.96803773
Maximum38.20168086
Range34.51808159
Interquartile range (IQR)10.55093162

Descriptive statistics

Standard deviation7.73891413
Coefficient of variation (CV)0.2747997489
Kurtosis-0.1573445069
Mean28.1620131
Median Absolute Deviation (MAD)4.438845165
Skewness-0.9276050673
Sum64688.1441
Variance59.89079191
MonotonicityNot monotonic
2023-08-27T15:38:17.834402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.882551671
 
< 0.1%
30.548346321
 
< 0.1%
32.202462961
 
< 0.1%
31.926644441
 
< 0.1%
31.646041111
 
< 0.1%
31.281605971
 
< 0.1%
30.741948111
 
< 0.1%
30.204133861
 
< 0.1%
30.849514151
 
< 0.1%
32.75590361
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
3.6835992661
< 0.1%
4.9152445061
< 0.1%
6.9331976161
< 0.1%
8.2290582571
< 0.1%
8.5251049861
< 0.1%
8.5756104451
< 0.1%
8.6257445081
< 0.1%
8.6762595111
< 0.1%
8.7005910121
< 0.1%
8.7263080141
< 0.1%
ValueCountFrequency (%)
38.201680861
< 0.1%
38.174901421
< 0.1%
38.16640271
< 0.1%
38.105976611
< 0.1%
38.0458561
< 0.1%
38.033013531
< 0.1%
38.029917731
< 0.1%
38.025918711
< 0.1%
37.989077721
< 0.1%
37.974090281
< 0.1%

MWh/ton CO2 net captured
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.329790442
Minimum0.5535142927
Maximum64.83838593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:18.073231image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.5535142927
5-th percentile0.6295954122
Q10.7607586594
median0.9570731841
Q31.359761603
95-th percentile2.843046385
Maximum64.83838593
Range64.28487164
Interquartile range (IQR)0.5990029433

Descriptive statistics

Standard deviation2.133317459
Coefficient of variation (CV)1.604250859
Kurtosis465.6135588
Mean1.329790442
Median Absolute Deviation (MAD)0.2416359772
Skewness18.79106342
Sum3054.528645
Variance4.551043379
MonotonicityNot monotonic
2023-08-27T15:38:18.683922image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4781503711
 
< 0.1%
0.64343430691
 
< 0.1%
0.67878946151
 
< 0.1%
0.6671041491
 
< 0.1%
0.65526478281
 
< 0.1%
0.65459993051
 
< 0.1%
0.68225866841
 
< 0.1%
0.63480036851
 
< 0.1%
0.655604251
 
< 0.1%
0.70155999211
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
0.55351429271
< 0.1%
0.55797643651
< 0.1%
0.56230350191
< 0.1%
0.56304774971
< 0.1%
0.56415893621
< 0.1%
0.56779341511
< 0.1%
0.56887740071
< 0.1%
0.57240519021
< 0.1%
0.57258172361
< 0.1%
0.57338028461
< 0.1%
ValueCountFrequency (%)
64.838385931
< 0.1%
47.099271931
< 0.1%
31.864415881
< 0.1%
25.954701731
< 0.1%
21.801563741
< 0.1%
16.97496181
< 0.1%
14.742562551
< 0.1%
12.923302651
< 0.1%
11.01246671
< 0.1%
10.54811471
< 0.1%

CO2 emitted/captured (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.40308806
Minimum9.060533051
Maximum92.10790668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:18.891833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.060533051
5-th percentile10.17914379
Q112.04434475
median14.6956509
Q319.66304034
95-th percentile33.85141808
Maximum92.10790668
Range83.04737363
Interquartile range (IQR)7.618695596

Descriptive statistics

Standard deviation8.567945005
Coefficient of variation (CV)0.4923232577
Kurtosis14.44169873
Mean17.40308806
Median Absolute Deviation (MAD)3.230226426
Skewness2.985861767
Sum39974.89327
Variance73.40968161
MonotonicityNot monotonic
2023-08-27T15:38:19.075108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.846914161
 
< 0.1%
10.37966381
 
< 0.1%
10.887903381
 
< 0.1%
10.720562921
 
< 0.1%
10.550374141
 
< 0.1%
10.540797761
 
< 0.1%
10.937463731
 
< 0.1%
10.254666721
 
< 0.1%
10.555262961
 
< 0.1%
11.212194931
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
9.0605330511
< 0.1%
9.1269079351
< 0.1%
9.1911810421
< 0.1%
9.2022267441
< 0.1%
9.2187133391
< 0.1%
9.2725960551
< 0.1%
9.2886542271
< 0.1%
9.3408756021
< 0.1%
9.3434872221
< 0.1%
9.3552991831
< 0.1%
ValueCountFrequency (%)
92.107906681
< 0.1%
89.449105011
< 0.1%
85.153501351
< 0.1%
82.369056831
< 0.1%
79.692468731
< 0.1%
75.342086161
< 0.1%
72.630193961
< 0.1%
69.93561241
< 0.1%
66.46820321
< 0.1%
65.50130831
< 0.1%

CO2 conc. To storage (mol%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2297
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9186797572
Minimum0.794163406
Maximum0.9327326701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:19.300504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.794163406
5-th percentile0.8655387239
Q10.9174603835
median0.9265038887
Q30.9303208234
95-th percentile0.932329562
Maximum0.9327326701
Range0.1385692641
Interquartile range (IQR)0.01286043988

Descriptive statistics

Standard deviation0.02074539049
Coefficient of variation (CV)0.02258174334
Kurtosis7.666325937
Mean0.9186797572
Median Absolute Deviation (MAD)0.004722356076
Skewness-2.702953553
Sum2110.207402
Variance0.0004303712265
MonotonicityNot monotonic
2023-08-27T15:38:19.497976image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.86908434041
 
< 0.1%
0.92688486461
 
< 0.1%
0.9268257351
 
< 0.1%
0.92676245551
 
< 0.1%
0.92669274051
 
< 0.1%
0.92661070721
 
< 0.1%
0.92651290621
 
< 0.1%
0.92679202761
 
< 0.1%
0.92696499951
 
< 0.1%
0.92693733371
 
< 0.1%
Other values (2287)2287
99.6%
ValueCountFrequency (%)
0.7941634061
< 0.1%
0.79666611261
< 0.1%
0.80175899291
< 0.1%
0.80886716191
< 0.1%
0.81465189731
< 0.1%
0.81828361621
< 0.1%
0.818330041
< 0.1%
0.82206543071
< 0.1%
0.8223678491
< 0.1%
0.82582932361
< 0.1%
ValueCountFrequency (%)
0.93273267011
< 0.1%
0.93273248791
< 0.1%
0.93273231961
< 0.1%
0.93273209521
< 0.1%
0.93273171181
< 0.1%
0.93273139491
< 0.1%
0.93273117521
< 0.1%
0.93273073251
< 0.1%
0.93273046811
< 0.1%
0.93273015561
< 0.1%

CO2 in feed (mol%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5216804528
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:19.668234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.5
Q30.7
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2541641982
Coefficient of variation (CV)0.4872028401
Kurtosis-1.178341957
Mean0.5216804528
Median Absolute Deviation (MAD)0.2
Skewness-0.1092360214
Sum1198.3
Variance0.06459943966
MonotonicityNot monotonic
2023-08-27T15:38:19.817139image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.6276
12.0%
0.7276
12.0%
0.9276
12.0%
0.8275
12.0%
0.5267
11.6%
0.4253
11.0%
0.3231
10.1%
0.2228
9.9%
0.1215
9.4%
ValueCountFrequency (%)
0.1215
9.4%
0.2228
9.9%
0.3231
10.1%
0.4253
11.0%
0.5267
11.6%
0.6276
12.0%
0.7276
12.0%
0.8275
12.0%
0.9276
12.0%
ValueCountFrequency (%)
0.9276
12.0%
0.8275
12.0%
0.7276
12.0%
0.6276
12.0%
0.5267
11.6%
0.4253
11.0%
0.3231
10.1%
0.2228
9.9%
0.1215
9.4%

CO2 in acid gas (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2275
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.4002999
Minimum26.65045975
Maximum405.3605514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:19.996948image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum26.65045975
5-th percentile27.60332468
Q191.40758048
median169.0394704
Q3250.6642552
95-th percentile343.610549
Maximum405.3605514
Range378.7100917
Interquartile range (IQR)159.2566747

Descriptive statistics

Standard deviation97.73696831
Coefficient of variation (CV)0.5540635043
Kurtosis-0.8881952004
Mean176.4002999
Median Absolute Deviation (MAD)79.36957948
Skewness0.2168083303
Sum405191.4888
Variance9552.514975
MonotonicityNot monotonic
2023-08-27T15:38:20.220019image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215.63146583
 
0.1%
309.44376342
 
0.1%
356.24377422
 
0.1%
33.725089372
 
0.1%
33.726556942
 
0.1%
33.72834742
 
0.1%
215.62792992
 
0.1%
402.80894312
 
0.1%
356.24175242
 
0.1%
262.60761922
 
0.1%
Other values (2265)2276
99.1%
ValueCountFrequency (%)
26.650459751
< 0.1%
26.662691441
< 0.1%
26.662940611
< 0.1%
26.663304681
< 0.1%
26.66391931
< 0.1%
26.669601511
< 0.1%
26.706977091
< 0.1%
26.707234581
< 0.1%
26.707595261
< 0.1%
26.708138191
< 0.1%
ValueCountFrequency (%)
405.36055141
< 0.1%
404.63268211
< 0.1%
404.05273171
< 0.1%
403.50395231
< 0.1%
402.95552231
< 0.1%
402.82032681
< 0.1%
402.81761121
< 0.1%
402.81552981
< 0.1%
402.8138181
< 0.1%
402.81235581
< 0.1%

CO2 from reboiler (kmol/hr)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2296
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.842502074
Minimum2.454545325
Maximum20.45454617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-08-27T15:38:20.417202image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.454545325
5-th percentile2.454545498
Q13.272727279
median4.295454543
Q35.318181805
95-th percentile18.40909034
Maximum20.45454617
Range18.00000084
Interquartile range (IQR)2.045454526

Descriptive statistics

Standard deviation4.416175392
Coefficient of variation (CV)0.7558705732
Kurtosis3.106547605
Mean5.842502074
Median Absolute Deviation (MAD)1.022727264
Skewness2.039477975
Sum13420.22727
Variance19.50260509
MonotonicityNot monotonic
2023-08-27T15:38:20.603939image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2727272742
 
0.1%
4.0909091131
 
< 0.1%
3.681818181
 
< 0.1%
3.2727271951
 
< 0.1%
3.0681818231
 
< 0.1%
2.8636363661
 
< 0.1%
2.6590908911
 
< 0.1%
2.454545451
 
< 0.1%
2.4545454581
 
< 0.1%
2.6590909021
 
< 0.1%
Other values (2286)2286
99.5%
ValueCountFrequency (%)
2.4545453251
< 0.1%
2.4545453341
< 0.1%
2.4545453381
< 0.1%
2.4545454141
< 0.1%
2.4545454231
< 0.1%
2.4545454291
< 0.1%
2.4545454321
< 0.1%
2.4545454341
< 0.1%
2.4545454361
< 0.1%
2.4545454371
< 0.1%
ValueCountFrequency (%)
20.454546171
< 0.1%
20.454545761
< 0.1%
20.454545481
< 0.1%
20.454545471
< 0.1%
20.454545471
< 0.1%
20.454545461
< 0.1%
20.454545461
< 0.1%
20.454545461
< 0.1%
20.454545461
< 0.1%
20.454545461
< 0.1%

Interactions

2023-08-27T15:38:09.328881image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:20.364329image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:23.457885image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:26.440369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:29.713828image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:32.859536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:36.004506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:39.667787image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:43.276740image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:46.494230image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:49.992276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:52.995300image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2023-08-27T15:37:35.369979image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:38.675285image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:42.652366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:45.832073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:48.983572image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:52.467308image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:55.647154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:58.539215image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:02.185019image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:05.424517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:08.803543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:12.502731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:23.072591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:26.059052image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:28.980472image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:32.420904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:35.612935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:38.894099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:42.851871image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:46.046592image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:49.180731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:52.644876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:55.842675image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:58.764760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:02.396986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:05.656696image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:08.980285image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:12.705003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:23.278976image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:26.234609image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:29.175104image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:32.650310image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:35.823953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:39.104949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:43.059480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:46.260663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:49.385945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:52.817773image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:56.061684image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:37:58.957018image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:02.604910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:05.878267image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2023-08-27T15:38:09.153864image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2023-08-27T15:38:20.828302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-08-27T15:38:21.216073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-08-27T15:38:21.612249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-08-27T15:38:21.986200image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-08-27T15:38:13.056303image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-27T15:38:13.483958image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexRunReboiler duty (W)Solvent circulation (kmol/hr)CO2 in feed (kmol/hr)Reboiler emission/capture emissionsCO2 captured (kmol/hr)CO2 emitted while capturing (kmol/hr)CO2 net captured (kmol/hr)CO2 net captured (%)MWh/ton CO2 net capturedCO2 emitted/captured (%)CO2 conc. To storage (mol%)CO2 in feed (mol%)CO2 in acid gas (kmol/hr)CO2 from reboiler (kmol/hr)
001100000040500.57530323.0521547.11087815.94127631.8825522.47815030.8469140.8690840.127.0501344.090909
112150000040500.64840823.2711419.46373813.80740327.6148063.80783640.6672710.8624860.126.7249296.136364
223200000040500.69670323.29256311.74362511.54893923.0978785.64920850.4179140.8619020.126.7090848.181818
334250000040500.72964823.29188614.0167169.27517018.5503408.39560360.1785350.8618590.126.70813810.227273
445300000040500.75340523.29241016.2896907.00272014.00544112.92330369.9356120.8618370.126.70759512.272727
556350000040500.77134723.29276518.5625804.7301869.46037121.80156479.6924690.8618210.126.70723514.318182
667400000040500.78537723.29302220.8353992.4576224.91524547.09927289.4491050.8618110.126.70697716.363636
71112400000060500.76126023.33727621.4954771.8418003.68359964.83838692.1079070.8266010.126.66269116.363636
81213350000060500.74486823.33697719.2224484.1145298.22905825.95470282.3690570.8266260.126.66294114.318182
91314300000060500.72408323.33649416.9493416.38715312.77430614.74256372.6301940.8266610.126.66330512.272727

Last rows

df_indexRunReboiler duty (W)Solvent circulation (kmol/hr)CO2 in feed (kmol/hr)Reboiler emission/capture emissionsCO2 captured (kmol/hr)CO2 emitted while capturing (kmol/hr)CO2 net captured (kmol/hr)CO2 net captured (%)MWh/ton CO2 net capturedCO2 emitted/captured (%)CO2 conc. To storage (mol%)CO2 in feed (mol%)CO2 in acid gas (kmol/hr)CO2 from reboiler (kmol/hr)
228724732474850000704500.239346150.69354214.528218136.16532330.2589610.5927529.6409030.9320460.9299.3049253.477273
228824742475900000704500.248217151.86939814.833069137.03632930.4525180.6013449.7669900.9320650.9298.1186233.681818
228924752476950000704500.256726153.05198615.138203137.91378330.6475070.6098099.8908900.9320820.9296.9380773.886364
2290247624771000000704500.264925154.20647515.441736138.76473930.8366090.61822210.0136760.9320990.9295.7854314.090909
2291247724781050000704500.272830155.34062415.744053139.59657131.0214600.62657010.1351810.9321150.9294.6531284.295455
2292247824791100000704500.280441156.47155616.046182140.42537431.2056390.63482410.2550150.9321290.9293.5227214.500000
2293247924801150000704500.287800157.57289216.346557141.22633431.3836300.64304010.3739650.9321430.9292.4225534.704545
2294248024811200000704500.294909158.66009416.646097142.01399731.5586660.65119210.4916720.9321560.9291.3362794.909091
2295248124821250000704500.301776159.73886216.945129142.79373331.7319410.65927010.6080190.9321680.9290.2579985.113636
2296248224831300000704500.308437160.78697317.242334143.54464031.8988090.66732410.7237130.9321790.9289.2107985.318182